A new method of multi-token prediction from Meta significantly enhances the speed and accuracy of large language models (LLMs). Traditional LLMs operate on a next-token prediction basis, predicting one token at a time. This method, while foundational, has shown limitations in learning efficiency and sensitivity to local patterns, requiring vast amounts of data to achieve human-like fluency and overlooking broader contextual understanding necessary for complex reasoning.
Researchers at Meta, Ecole des Ponts ParisTech, and Université Paris-Saclay have developed an approach that allows LLMs to predict several tokens simultaneously from any given position within the training data. This technique, which leverages the Transformer architecture with minimal modifications, does not demand additional training time or memory resources. By predicting multiple tokens at once, the model achieves higher sample efficiency, meaning it can learn more from less data, and improves its ability to grasp longer-term patterns.
The findings show that this multi-token prediction method not only accelerates model inference by up to three times but also enhances performance, particularly in larger models and specific tasks like code completion. This improvement is crucial for applications requiring rapid processing and high accuracy, offering a significant advantage in efficiency and effectiveness without increasing operational costs.
Why Should You Care?
Multi-token prediction accelerates AI language models up to 3X, enabling better performance on generative tasks.
– Triple speeds and improved accuracy
– Overcomes limitations of next-token prediction for language acquisition and reasoning
– Requires no extra training time or memory overhead
– Enables faster inference with a wide range of batch sizes
– Promotes learning longer-term patterns, even with byte-level tokenization